The Cryosphere and Global Environmental Change

54 CHAPTER 2

2.3 CONCLUSIONS

This chapter has examined, albeit briefly, someof the in situ data sets available for analyz-ing the state of the cryosphere. In situ mea-surements provide an accurate representationof state variables at the point or microscaleand have enabled our understanding ofenergy and mass transfer processes at thatscale. Countless research projects in geo-graphical, earth, and engineering scienceshave yielded high quality data sets. However,the combined spatial and temporal coverageof these field campaigns remains limited.The ability of these data sets to comprehen-sively represent regional to global cryosphericprocesses is uncertain at best. For example,our understanding of snowpack energy andmass physical exchange dynamics suggeststhat these processes can be resolved at scalesof 100 m or so in the mountains and perhaps12 km over more homogeneous terrain.Quantifying bulk snow properties usingsparse in situ networks of point measure-ments is unlikely to yield accurate spatialrepresentation of snow water equivalent orother bulk variables at the regional scale.Intensive field measurement programs,usually conducted for short durations, canrepresent processes at the local scale well.However, the link to regional and globalscales is uncertain in most cases. Our abilityto represent other cryospheric state vari-ables from local to global scales of variabil-ity also suffers from a similar disconnect.

In situ measurements alone, therefore,are inadequate to quantify global cryospheric

changes. At the same time, many publiclyfunded operational in situ measurementnetworks are in decline and the need forunderstanding of the cryosphere in globalenvironmental change context has neverbeen greater. The fact is that in situ mea-surements, when used with other monitor-ing approaches, can make a significantcontribution to global cryospheric under-standing. For example, remote sensing is anapproach that has found a strong niche incryospheric science. Coupling in situ mea-surements with remote sensing observations,either for calibrating retrieval algorithmsand models, or validating remote sensingproducts, is of critical importance to thecredibility of applying remote sensing datato global cryospheric change. Furthermore,the use of numerical models for the simu-lation of cryospheric physical processesrequires both remote sensing and in situmeasurements in the model developmentphase (conceptual design or model formu-lation), the implementation phase (as forc-ing fields for the model or perhaps throughcomplex data assimilation schema), and atthe validation phase (to quantify accuracyand precision). A strong reason exists,therefore, for continued in situ cryospheremeasurements. However, no longer shouldwe expect in situ data alone to representcryospheric state variables globally. Theyshould be used within the context of theirinherent spatial and temporal constraints aswell as to develop and improve remotesensing and/or numerical simulation mod-els of the cryosphere.

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3.1 INTRODUCTION

The connecting links between the large-scale cryospheric changes discussed inChapter 1 and the intricacies of measure-ment, monitoring, and modeling discussedin Chapter 2 are the concepts of mass andenergy budgets. Regions of the Earth wheresurfaces and volumes of snow and ice areprevalent display ways of storing water and regulation of energy that are unique.By measuring the inputs, throughputs, andoutputs of mass and energy into, through,and out of snow- and ice-covered areasover unit time periods, it becomes possibleto account for the availability, storage, anddistribution of that mass and energy. Theinterpretation of the unique behavior of the mass of snow and ice is the domain ofthe snow and ice hydrologist (e.g. Kuzmin1961) and the interpretation of the pro-cesses associated with energy exchange arethe domain of the boundary layer climatol-ogist (e.g. Oke 1987).

Snow reflects about 80% of the sunsradiation, whereas soil and sea water absorb80% or more. This simple fact means thatthe amount of land (and sea ice) covered bysnow is critically important to the Earthsradiation balance and so to the global cli-mate system. Changes in radiation balanceare extreme when the snow cover is firstestablished and when it disappears. In lati-tudes above 65N, the rapid increase insolar radiation in spring coincides with therapid melting of shallow snowpacks and

this produces extreme changes in surfaceradiation balance over periods of as little as10 days.

3.2 SNOW AND ICE AS ENERGY REGULATORS

The energy regulation functions (describedbelow) profoundly alter the climate of thesnow and ice, by comparison with theatmosphere, ocean, and soils which sur-round them (Fig. 3.1).

Snow and ice function as energy banks:they store and release energy. They storelatent heat of fusion, sublimation, and crys-tal bonding forces. To sublimate 1 kg ofsnow requires the same amount of energyas raising the temperature of 10 kg of liquidwater by 67C; by comparison, to melt 1 kgof snow (already at 0C) requires the sameamount of energy as raising the tempera-ture of 1 kg of water by 79C.

Snow and ice also function as radiationshields: cold snow reflects most shortwaveradiation and absorbs and reemits most long-wave radiation. As snowmelt progresses, thesnow cover reflects less shortwave radiationbecause of a change in its physical proper-ties. The proportion of shortwave radiationreflected from a snow or ice cover is highcompared with soil and vegetation. Baresoil and vegetation will absorb as much as eight times shortwave radiation as a freshsnow cover. Shortwave radiation that is not reflected is absorbed in the top 30 cm of the snowpack. Snow cover behaves

3

PROCESSES OF CRYOSPHERIC CHANGE

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56 CHAPTER 3

almost as a black body. The longwave radiation is absorbed and reradiated as ther-mal radiation. The wavelength of emissiondepends on the surface temperature of thesnow cover.

Snow and ice also function as insulators.They are porous media with high insulationcapacity. This insulation can result in strong temperature gradients that restruc-ture the snow composition. The thermalconductivity of a snow cover is low com-pared with soil surfaces and varies with thedensity and liquid water content of thesnow cover (Table 3.1). From the table itcan be calculated that a surface temperaturewave of a given magnitude would penetrate 2.5 times as deeply into granite comparedwith fresh snow.

3.2.1 THE ENERGETICS OF THE SNOWSURFACE

The concept which is commonly used tosummarize the effects of energy regulationat the surface is the energy balance, in thefollowing form:

Q* = QH + QE + QG (3.4)

where Q* is the net all-wave radiation fluxdensity, QH is the turbulent sensible heatflux density, QE is the turbulent latent heat flux density, and QG is the subsurfaceheat flux density all in the form of W m2.Energy used in melting snow is normallyaccounted separately from latent (evapora-tive) heat because for continuous snow

FIG. 3.1 Mass and energy fluxes controlling the energetics of a snow cover and their relation tosnowpack structure properties and processes, the atmosphere, and the ground (from Pomeroy &Brun 2001).

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PROCESSES OF CRYOSPHERIC CHANGE 57

cover in open environments, the proportionof total phase change energy is so muchgreater than evaporation (c. 90% accordingto Shook & Gray 1997). In most places,incoming shortwave radiation is the princi-pal source of energy but other processes caninvolve energy exchanges of similar ordersof magnitude. Much of the solar radiationincident on a snow surface is reflected withalbedos as high as 0.9 for compact, dry, clean

snow, dropping to 0.50.6 for wet snow, and0.30.4 for porous, dirty snow. Albedo decayswith time since the previous snowfall, butwith very different decay rates for shallowand deep snowpacks.

Over snow surfaces exposed to the atmo-sphere, outgoing longwave radiation is usu-ally larger than incoming radiation, leadingto a net loss of longwave radiation from thesnow cover; by contrast, under forest

TABLE 3.1 Thermal regime (J.R. Mackay, personal communication).

c

Typical thermal regime values (CGS units)

Gravel 0.003 2.0 0.18 0.008 0.09Icy silt 0.006 1.6 0.31 0.012 0.11Dry peat 0.0004 0.4 0.5 0.002 0.04Ice 0.005 0.9 0.5 0.012 0.11Icy peat 0.005 0.9 0.4 0.012 0.11Snow (fresh) 0.0002 0.2 0.45 0.002 0.04Snow (packed) 0.0005 0.3 0.45 0.004 0.06Water 0.0013 1.00 1.00 0.0013 0.036Granite 0.006 2.7 0.19 0.013 0.11Wet mud 0.0006 0.3 0.45 0.004 0.06

Handy equationsDepth of freezing:z = bt (3.1)Rate of freezing:b/2t (3.2)Depth to which temperature of ground perceptibly decreases in time, t:z = 12 t (3.3)

Definitions thermal conductivity; the quantity of heat that flows in unit time through a unit areaof a plate of unit thickness having unit temperature difference between its faces (cal (cm sec C)1 or 418.4 W (m K)1). density; mass per unit volume (g cm3, multiply by 1,000 for kg m3).c volumetric specific heat or heat capacity, amount of heat necessary to change the tem-perature of a unit mass by one degree (cal (g C)1, multiply by 4.184 for J (kg K)1). thermal diffusivity; k/c determines the rise in temperature (cm2 s1, multiplyby 0.01 for m2 s1).b a proportionality factor which is a function of soil characteristics.L specific latent heat of fusion of ice (80 cal (g C)1; 334 kJ kg1).t unit time.z depth of freezing or temperature change.

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58 CHAPTER 3

canopies, a downward net longwave fluxcan develop. The radiation balance oversnow shows wide variations as a function ofcloud cover, forest canopy, and topography.

The turbulent fluxes of sensible and latentheat are also important for the energy bal-ance of a snow cover. They depend respec-tively on the vertical gradients of temperatureand humidity in the atmosphere and turbu-lent transfer of heat and water vapor at thesnow surface. Because gradients of meteo-rological variables are rarely available, bulktransfer calculations are necessary.

For discontinuous snow covers in openenvironments, local advection of sensibleenergy from bare ground to snow patchesbecomes an important component of theenergy balance. The ground heat flux tosnow on a daily basis is considered to be asmall component of the energy balance. But,because it is persistent, it can have an impor-tant cumulative effect early in the melt sea-son in retarding or accelerating the time ofmelt and in affecting the environmentbetween snow patches. In locations withincompletely frozen soils, the ground heatflux is positive; however, over permafrostsoils the flux is negative in late winter. Thisnegative ground heat flux is important indelaying the snowmelt season in the North, inspite of high sensible and radiant heat fluxes.

3.2.2 THE ENERGETICS OF THE SNOWPACK

Oke (1987) has explained that the energybalance of a snowpack is complicated notonly by the fact that shortwave radiationpenetrates into the snowpack but also byinternal water movement and phasechanges. He invites comparison of a snow-pack volume in terms of energy balance of(a) a frozen snowpack and (b) a meltingsnowpack with (c) the water balance of thatsame control volume (Fig. 3.2).

The energy balance of a snow volumedepends upon whether it is a cold (0C)

or a wet (0C, often isothermal) snowpack.When considering a snow volume, eqn. 3.4has to be replaced by

Q* = QH + QE + QG + QS + QM (3.5)

where QS is the net heat storage changeand QM is the latent heat storage changedue to melting or freezing. In the case of acold snowpack (Fig. 3.2a) such as is com-monly found at high latitudes in winterwith little or no solar input, QE and QM arelikely to be negligible because there is noliquid water for evaporation, little atmo-spheric vapor for condensation or sublima-tion, and both the precipitation and thecontents of the snowpack all remain in the solid phase. Similarly, heat conductionwithin the snow will be small because ofthe low conductivity of snow (Table 3.1)and the lack of solar heating, so that QS andQG are also negligible. The energy balancetherefore reduces to that between a netradiative sink (Q*) and a convective sensible(QH) heat source (Fig. 3.2a).

FIG. 3.2 Schematic depiction of the fluxesinvolved in the energy (a and B) and waterbalances (c) of a snowpack volume (from Oke1987). The energy balances are for (a) cold orfrozen pack, (b) wet or melting pack.

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In the case of a wet snowpack duringthe melt period (Fig. 3.2b), the surface tem-perature will remain close to 0C, but theair temperature may be above freezing.Precipitation may then be as rain and theenergy balance is further complicated, asfollows:

Q* + QR = QH + QE + QG + QS + QM(3.6)

where QR is heat supplied by rain with atemperature greater than that of the snow.

There is another factor which should beborne in mind and that is the infiltration ofmeltwater into soils, involving a significantenergy flow. If meltwater refreezes in frozensoils, there will be a significant release oflatent heat and further melting of frozensoil-water. Dry snow experiences thermalconduction, thermal convection, and windpumping which govern the heat fluxbetween bottom and top of the snow cover.

Temperature gradients induce watervapor gradients and consequent diffusion ofwater vapor from warmer parts to the colderones. When temperature gradients exceed10Cm1, destructive ice crystal sublimationand recrystallization occur as water vapormoves along the gradient.

The rate of snowmelt is primarily con-trolled by the energy balance near the uppersurface where melt normally occurs first. In temperate climates, snowpacks tend to beuniformly close to the melting temperaturewhen melt commences at the surface. Incold climates, however, change in internalenergy of the snowpack may be significant.

In an alpine, midlatitude region, the rel-ative importance of each of the energy bal-ance terms varies with season (Fig. 3.3).Note in particular the changing dominanceof the shortwave radiation versus longwaveradiation from December to April; the rela-tive importance of the sensible heat versuslatent heat and the comparatively small

importance of the rainfall energy (Brun et al.1989). Although snow cover reduces theavailable energy at its surface because of itshigh albedo to solar radiation and highemissivity of longwave radiation, its insula-tive properties exert the greatest influenceon soil temperature regime (Table 3.2). Snowcover acts as an insulating layer whichreduces the upward flux of heat, resultingin higher ground temperatures than wouldoccur if the ground were bare (Fig. 3.4).Judge (1973) estimated that on the average,ground surface temperatures in Canada are3.3C higher than air temperatures.

The exact value of this insulation effectcan be calculated from eqn. 3.7 in Table 3.2.The water balance of a snow or ice volume(Fig. 3.2c) is given by

S = P (E + Q) (3.8)

where S is the change in snow or ice stor-age, P is precipitation, E is evaporation, andQ is discharge, all expressed as millimeterper annum. Further discussion of this equa-tion will be delayed until the second half ofthis chapter.

3.2.3 THE ENERGETICS OF GLACIERS

The energy balance at the surface of aglacier is the sum of the individual energycomponents and may be expressed as

QM = Q* + QH + QE (3.9)

where QM is energy available to melt ice. Q*is frequently broken down into net short-wave (QS) and net longwave radiation (QL)flux densities because the relative impor-tance of each component varies so muchfrom place to place (Paterson 1994).

In glacier energy balance studies in con-tinental locations, net radiation has beencalculated to account for approximately 66%of ablation energy (e.g. Braithwaite 1981);

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60 CHAPTER 3

however, in more maritime climates, suchas Iceland, net radiation may account for aslittle as 10% (e.g. Ahlmann & Thorarinsson1938) because of the presence of warmerand moister air masses. QS is highest on low-latitude, high-altitude glaciers in theAndes; QL is highest in humid conditionsand close to rock surfaces and valley sides;

QH and QE become important where warm,humid air moves over glacier surfaces suchas western North America, southwestIceland, western Norway, the Pacific coastof Chile (Laumann & Reeh 1993), and theglaciers of equatorial Africa (Hastenrath &Kruss 1992). The effects of debris cover on a glacier are interesting. Debris having

FIG. 3.3 Daily variation ofenergy balance components ofan alpine snow cover in theFrench Alps during three periods. (a) mid-winter, (b) late winter, and (c) spring(from Brun et al. 1989).

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a lower albedo than ice will absorb more QSand encourage higher ablation; but debriscan also protect underlying ice from abla-tion by shielding it from QS. Protection willoccur if the debris cover is thick enough toprevent heat from the surface being con-ducted through to the ice in the course of adaily temperature cycle. The crossoverpoint, where ablation is at a maximum,occurs when the debris is 0.51 cm thick(Ostrem 1959).

The same general energy budget equationas that which is used for snow is appropri-ate for glaciers. Over melting glaciers, netshortwave radiation is generally the domi-nant term (Greuell & Smeets 2001), the

latent heat flux is relatively small, and netlongwave radiation and the sensible heatflux are of intermediate magnitude (Fig. 3.5).The subsurface heating and the heat fluxsupplied by rain is commonly negligiblewhen averaged over a year but specificcombinations of cold glaciers and intensewarm rainstorms must be considered sepa-rately (Hay & Fitzharris 1988).

A part of the shortwave radiative fluxwill be absorbed below the surface of theice. Water formed by melt or deposited onthe glacier as rain will penetrate into thesnow and/or ice. On its way down, some of the water will be retained in the poresowing to capillary forces. If the water

TABLE 3.2 Effect of snow cover on the mean annual ground temperature at the bottom ofthe active layer (modified from Kudryavtsev 1965).

Thickness of snow z (m)

Snow Thermal 0.1 0.2 0.3 0.4 0.6 0.8 1.0density diffusivity ()

0.075 0.0010 0.094 0.181 0.259 0.329 0.451 0.551 0.6320.110 0.0015 0.081 0.155 0.224 0.288 0.400 0.491 0.5720.150 0.0020 0.071 0.136 0.197 0.253 0.355 0.442 0.5180.190 0.0025 0.064 0.123 0.178 0.230 0.324 0.407 0.4800.225 0.0030 0.058 0.113 0.164 0.213 0.302 0.381 0.4500.250 0.0035 0.054 0.105 0.153 0.198 0.282 0.357 0.4250.300 0.0040 0.051 0.098 0.143 0.186 0.267 0.338 0.4030.340 0.0045 0.048 0.093 0.136 0.178 0.254 0.323 0.3860.380 0.0050 0.045 0.088 0.130 0.169 0.242 0.309 0.3710.415 0.0055 0.043 0.081 0.124 0.161 0.232 0.297 0.356

TA = T + T0 (1 ) (3.7)

The value of (1 ) is:

f = e0.018 z T

TA: mean annual ground temperature, at the bottom of the active layer at a given time (C).T: mean annual air temperature (MAAT) for the period.T0: yearly amplitude (half range) of mean monthly air temperatures (C T0) for the period.z: mean maximum snow depth (cm).T: a period of one year, in hours (8,760).: thermal diffusivity ( k/pc) of snow (m2 hr1). Refer to Table 3.1 for definitions.

1

f

1

f

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62 CHAPTER 3

encounters cold snow, it will refreeze andlocally increase the temperature. The waterthat refreezes within the snow is calledinternal accumulation. If the water encoun-ters ice on its way downward it will gener-ally from runoff, thus contributing toablation. However, if the slope is small, partof the water will accumulate on top of theice. There it may freeze as so-called super-imposed ice if the underlying ice is cold.

3.2.4 THE ENERGETICS OF SEA ICE ANDVARIOUS TERRAIN TYPES

The energy balances of snowpacks andglaciers are comparatively simple whencompared with those of sea ice and variable

terrain types because of the high spatialvariability of the sea ice and the terrain sur-faces. As indicated in Chapter 1, there aremany varieties of sea ice and each of themhas different spectral albedos. Large changescan occur rapidly over the course of a sum-mer melt season and also during the fallfreeze back. Perhaps the most importantaspect of the temporal behavior of the spec-tral albedos of sea ice is the general decreasethat takes place with the onset of the meltseason. In a study reported by Grenfell andPerovich (1984), shortwave radiation fluxdensities were measured near Point Barrow,Alaska on shorefast ice, first and second yearfloes and a deformed rubble zone betweenMay 18 and June 17, 1979. This represents

FIG. 3.4 Thermal regime of a boreal forest snowpack. Temperature was measured half-hourly atvarious heights above the ground in Prince Albert National Park, Saskatchewan. Temperatures measured at heights greater than the snow depth are air temperatures. (a) Early winter, 10 cm deep,100 kg m3 dense. (b) Early mid-winter, 20 cm deep, 150 kg m3 dense (from Pomeroy & Brun 2001).

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the transition from cold winter conditionsto well-developed melt conditions. Theydocumented three separate evolutionarysequences which are graphed separately inFigs. 3.6a, 3.6b, and 3.6c.

At the scale of the polar regions, terraintypes present similar problems in terms ofspatial variability. In an oft-quoted study byWeller and Wendler (1990) closed borealforests, open woodlands, tundra, the packice of the Arctic Ocean, glaciers, and largeice sheets were compared in terms of theirdistinctive energy budgets during summerand winter (Fig. 3.7). Large differences areshown between energy balances duringsummer with the boreal forest and the tun-dra acting as major heat sources for the sur-rounding terrain, and the glaciers acting asmajor heat sinks. In winter, thin pack ice is the major source of heat energy. In the

Antarctic, summer conditions are quite dif-ferent. Because there are few exposed rocksurfaces and no tundra the albedo remainshigh everywhere and the energy budget isquite low in summer. Little melting occurson the slopes and the plateau of theAntarctic Ice Sheet.

3.2.5 PERMAFROST

Permafrost is defined as ground thatremains at or below 0C for at least twoconsecutive years. This means that moisturein the form of either water or ice may ormay not be present. Permafrost may there-fore be unfrozen, partially frozen, or frozendepending on the state of the ice/watercontent.

There is a tendency to regard a frozen soilas one in which the water has beenreplaced by ice. In fact at most temperaturesof interest, frozen soils contain ice andwater. The more fine-grained a soil is, thegreater is the amount of water remaining ata given negative temperature. As the watercontent is reduced by progressive formationof ice, the remaining water is under anincreasing suction. The suction developedby freezing can be seen from the observa-tion that a previously unfrozen clay sample,after being frozen and then thawed, showsa completely changed structure. It consistsof hard almost shaly flakes. The clay flakeshave consolidated because of the effectivestress associated with the suction. Thischanged structure means that the bulkstrength is lowered as the discontinuitiesbetween the flakes are planes of lowstrength.

Another critically important implicationof suctions developed in the ground onfreezing is the phenomenon of frost heave.Water is drawn toward the freezing soiland, on entering the frozen zone, becomesice. The frozen soil then contains more iceand has a higher moisture content than

FIG. 3.5 Components of the surface energybalance derived from mid-summer balance onthe western part of the Greenland Ice Sheet andon the Pasterze glacier, Austria. Average values(a) over 38 days at Camp IV just below ELA;(b) over 70 days at Carrefour in the ice sheetaccumulation zone; and (c) and (d) over 46 daysat the Pasterze on both sides of the ELA (fromAmbach 1979; Greuell & Smeets 2001).

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FIG. 3.6 Spectral albedos observed over snow, bare ice, melt ponds, snow covered ice, and meltingfirst year ice (from Grenfell & Perovich 1984).

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before freezing. Consequently the volumeof the soil increases and this results in frostheave. This effect is over and above the 9%expansion that occurs when water freezes.Frost heave occurs preferentially in siltysoils and does not occur in coarse grainedmaterials. Clay rich soils have such a lowpermeability that water migration is reducedby comparison with the silty soils. Again,the practical significance of frost heave lies in the great loss of strength that occurswhen frost heaved soils thaw. Foundationsfor roads and pipelines in permafrost regionsrequire large quantities of coarse grainedmaterials to reduce the heaving during winter.

As French (1996) points out, there arethree major considerations related to thewater/ice content of permafrost:

1 The freezing of water in the active layer atthe beginning of winter each year results

in ice lensing and ice segregation. Theamount of heave will vary according tothe amount and availability of moisture inthe active layer, with poorly drained siltysoils showing the maximum heave effects.There is also secondary heave effect asunfrozen water progressively freezes. Thismoisture migrates in response to a tem-perature gradient and causes an ice-richzone to form in the upper few meters ofpermafrost (Mackay 1983).

2 Ground ice is a major component of permafrost, particularly in unconsolidatedsediments (Mackay & Black 1973). Ifground ice-rich permafrost thaws subsi-dence of the ground results. A range ofprocesses associated with permafrostdegradation are summarised under theterm thermokarst.

3 The hydrological and groundwater condi-tions of permafrost terrain are unique(Hopkins et al. 1955). Subsurface flow isrestricted to unfrozen zones called taliksand to the active layer.

There are three groups of features whoseformation necessarily involve permafrostand which therefore are diagnostic of per-mafrost conditions: (a) Patterned ground,including ice wedge polygons (Fig. 3.8),stone polygons, sorted circles (Fig. 3.9),


FIG. 3.7 Energy balance over various terraintypes in the polar regions (from Weller &Wendler 1990).

FIG. 3.8 Patterned ground on Jan MayenIsland, illustrating high centered polygons,1978 (photo by the late Alfred Jahn).

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sorted stripes, and nonsorted circles; (b)palsas; and (c) pingoes. Permafrost terrain isgenerally regarded as highly sensitive tothermal disturbance. Mackay (1969) hassummarized some of the major processesinvolved (Fig. 3.10). The fundamentalpoints made by Mackay are (a) the distinc-tion between severe and minor disturbanceand (b) the association of severe distur-bances with frost susceptible soils and highground ice content.

3.3 SNOW AND ICE RESERVOIR FUNCTIONS

Snow and ice function as storage reservoirs(Figs. 3.1 and 3.2c). They are reservoirs ofwater that profoundly alter hydrology.During melt, snow and ice move as melt-water in preferential pathways within thesnowpack or ice mass. Runoff and stream-flow generation will differ depending onwhether the underlying soil is frozen orunfrozen.

Mass budgets are generally shown as

I O = S (3.10)

where I is an input term, O is an outputterm, and S is the change in storage term.

But what is often overlooked is that such aformulation ignores the time integrationover which the storage change occurs andthe relevant area.

A more satisfactory hydrologic book-keeping equation is

t1t2 Pdt (t1t2 Qdt + t1t2 Edt)=

(3.11)

where Q is instantaneous discharge from abasin; t1

t2 Pdt is precipitation over the basinbetween t1 and t2; t1

t2 Edt is evaporation andtranspiration over the basin between t1and t2. Selection of the time period betweent1 and t2 affects the dimensions of the storage term; selection of the size andhomogeneity of the drainage basin hasimplications for the resolution level of thebudget.

3.3.1 MASS BUDGET FOR SNOW

If one is interested in the within-seasonmass budget of snow, it is common to rear-range the equation as follows

SSnow = t1t2PSnowdt (t1t2 Qdt + t1t2 Edt)

(3.12)

and to solve for change in snow storage(SSnow).

In the illustrative example (Fig. 3.11),interest attaches to the monthly and annualbalance of water and ions in Jamieson Creekbasin in the Coast Mountains of BritishColumbia (Zeman & Slaymaker 1978).

The annual mass budget under most cir-cumstances has no net storage of snow andreduces to

Pannual = Qannual + E annual (3.13)

However, on glaciers in the accumulationzone and in high-latitude polar regions

dSdt

66 CHAPTER 3

FIG. 3.9 Sorted stone circles in HornsundFjord, Svalbard (photo by the late Alfred Jahn).

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FIG. 3.10 Disturbance of permafrost (Mackay personal communication).

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FIG. 3.11 Flow diagram for mass balance model of monthly and annual balances for snow coverin Jamieson Creek basin approximately 25 km north of Vancouver, British Columbia (from Zeman& Slaymaker 1978).

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there will be a net snow storage term andeqn. 3.8 will be needed.

3.3.2 MASS BALANCE FOR GLACIER ICE

The within-season mass balance for glacierice is normally written as

b = c + a (3.14)

where b (mass balance), c (accumulation),and a (ablation) are expressed as equivalentvolume of water.

Commonly, the annual mass balance of aglacier is determined with the aid of runoffmeasurements (Q) from the snout of theglacier combined with the monitoring ofstakes arranged at a number of elevationson the glacier, precipitation gauges (P), anda micrometeorological station to determineevaporation (E). The form of this equationis then

Bn = P E Q (3.15)

This is the hydrological method. Bn is themass balance at the end of the balance year.This is usually subdivided into a winter balance Bw and summer balance Bs. In theexample illustrated (Fig. 3.12), the net massbalance of a number of glaciers on Svalbardis compared over the period 19502000(Dowdeswell et al. 1997).

3.3.3 THE MASS BALANCE OF AN ICE SHEET

The spatial scale of an ice sheet determinesthe need for accurate representation of spatial variability of ice mass. This problemwas intractable until satellite-based remotesensing techniques became available. Theform of the relevant mass balance equation is

Bn = Ma Mm Mc Mb (3.16)

where Ma is annual surface accumulation;Mm is annual loss by glacial surface runoff;Mc is annual loss by calving of icebergs; and Mb is the annual balance at the bottom(melting or freeze-on of ice). Equation 3.16suggests that the total mass balance can beobtained by two methods: (a) by directmeasurement of the change in volume bymonitoring surface elevation change and(b) by the budget method, determining eachterm on the right-hand side of the equationseparately. Figure 3.13 shows an example ofestimated snow accumulation rates derivedfrom both historical and recent data (Baleset al. 2001).

3.3.4 MASS BALANCE OF SEA ICE

The predominant feature of the Arcticsphysical environment is the presence of asea ice cover which is perennial in the cen-tral Arctic and at least seasonal in themarginal seas (Laxon et al. 2004). Sea icesrelatively straight forward (compared toland snow cover) and rapid (compared to land ice sheets and glaciers) response toatmospheric forcing suggests that observa-tions of sea ice cover may provide earlystrong evidence of global warming in theArctic. Moreover, the sea ice cover is a spatially integrated indicator of environ-mental change by contrast with the spottytemperature records available for the Arctic.

On the other hand, the dynamic responseof sea ice to environmental change dependson a complex interplay of mechanical andthermodynamic processes. Because of icedeformation, a typical 100 km2 patch of seaice will contain a variety of ice thicknesses.These thicknesses range from open water tovery thick ice, including pressure ridgesextending possibly 30 m or more below thesurface. On top of this matrix there is oftena relatively thin snow cover. Although thin,this snow cover can cause substantial insu-lation of the ice and reduce its growth rate.


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This spatial heterogeneity, especiallyaround the edges of the sea ice, makes themass balance of the sea ice the most com-plex mass balance in the cryosphere. Localgrowth and melt and horizontal transport

and deformation alter the local mean thick-ness (ice volume per unit area) and involveexchanges of mass (fresh water) and energywith the atmosphere and ocean. Plate 3.1provides an illustration based on results of

FIG. 3.12 Net annual mass balance for Svalbard glaciers. (a) For 13 glaciers, (b) for 7 glaciers witharea 6 km2, (c) for glaciers that calve into the sea (from Dowdeswell & Hagen 2004).

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a sea ice model (Hilmer et al. 1998). In this figure, the annual mean ice transport isgiven by the vectors, the annual meanthickness by the solid contours, and the netfreezing rate (net ice growth minus icemelt; directly proportional to the salt fluxdelivered to the ocean surface) by the col-ored shading. The general pattern is anticy-clonic circulation within the Arctic basinand outflow of ice through the Fram Straitthat is balanced by net ice growth overmuch of the basin. Mean transport pattern

leads to convergent deformation and thick-ening along the Canadian Arctic islands and Greenland, with divergence and corre-spondingly thin ice along the centralEurasian coast (Flato 2004).

3.4 SNOWFALL

Snow forms in clouds when the tempera-ture is less than 0C and supercooled waterand cloud condensation nuclei are present.Ice crystals form around cloud condensa-tion nuclei and grow through aggregationof small ice crystals and riming from waterdroplets into the snowflake form. For snow-fall to occur there must be sufficient depthof cloud to permit the growth of snow crys-tals and sufficient moisture and aerosolnuclei to replace those removed from thecloud in falling snowflakes.

The effects of wind redistribution on theevolution of a snow cover are most obviousin open environments. Four processes areinvolved: (a) erosion of snow cover, (b)transport of blowing snow, (c) sublimationof blowing snow in transit, and (d) deposi-tion of snow. Dyunin et al. (1991) notedthat wind redistribution of snow is the pri-mary process of desertification in steppeenvironments and they associate the phe-nomenon of northern desertification withsuppression of vegetation in the Russiansteppes and forests and the resultingincrease in frequency of blowing snow.

3.4.1 INTERCEPTION BY VEGETATION

Snow interception is controlled by accumu-lation of falling snow in a forest canopy. Thesnow is subsequently affected by subli-mation, melt, and unloading of snow bycanopy branches and wind redistribution.Intercepted snow receives snow from snow-fall, snow unloaded from upper branches,drip from melting snow on upper branches,

FIG. 3.13 Estimated snow accumulation rateson Greenland derived from historical andrecent data after rejection of those considereddubious or those which referred to very shorttime periods (from Ohmura & Reeh 1991;Bales et al. 2001).

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and vapor deposition during supersaturatedatmospheric conditions. Intercepted snowcan sublimate to water vapor or becomesuspended by atmospheric turbulence, fol-lowed by further sublimation or depositionto surface snow. Deposition to the surfacemay also occur by melt and drip to the surface or by direct unloading from thebranches. Hence, intercepted snow mayreach the ground as a solid, liquid, or vapor,but not all intercepted snow eventuallyreaches the ground (Fig. 3.14). The inter-ception efficiency of a canopy is the ratio ofsnowfall intercepted to the total snowfalland this efficiency is an integration of thecollection efficiencies for individual branches.The collection efficiency of a branch is lim-ited by three factors: (a) elastic rebound ofsnow crystals falling on to snow and/orbranch elements; (b) branch bending undersnow load; and (c) strength of the snowstructure. In general, canopy scale inter-ception efficiency declines with snowfallamounts; branch scale collection efficiencyis low for low snowfall, high for mediumsnowfall, and low for high snowfall.

The net effect of interception processes isthat under a forest canopy both snow depthand water equivalent vary.

3.4.2 SNOW ACCUMULATION

The areal extent of snow varies morerapidly and more dramatically than that ofany other widely distributed material onEarth. In the Northern Hemisphere, themonthly mean area covered by snow onland ranges from 5 (106) to 4.7 (107) km2 asbetween the Northern hemisphere summerand winter.

Snow cover is the net accumulation ofsnow on the ground and is the end productof both accumulation and ablation pro-cesses. It is therefore the product of com-plex factors. The areal variability of snowcover is commonly considered on four spa-tial scales: global (106108 km2); regional(macroscale) with areas of up to 106 km2

(102106 km2); local (mesoscale) from 102

to 102 km2; and microscale from 106 to102 km2.

At global scale, snow cover duration islongest near the poles and on high moun-tains. Because early and late seasonal snowsare ephemeral, analysts often have difficultyin deciding exactly what criteria determinethe length of the winter snow cover period.This problem may be partially overcome byrejecting ephemeral occurrences. But differ-ent assumptions make it difficult to com-pare maps of snow cover distribution.

Factors controlling snow cover distribu-tion and characteristics include temperature(colder temperatures associated with rela-tively dry and light snow), wind (redistribu-tion as snow drifts), forest environments(openings versus within stand locations andtransport of intercepted snow), physiogra-phy (elevation, slope, aspect, roughness).The average annual snow accumulation inthe upper Columbia River basin (Fig. 3.15)illustrates the influence of physiography well.In the same region of western Canada, theinfluence of continentality and elevation onsnow-load and water equivalence has beenwell documented (Schaerer 1970; Fig. 3.16)

FIG. 3.14 Mass fluxes associated with the disposition of winter snowfall in a boreal forest(from Pomeroy & Schmidt 1993).

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Trends in snow cover in the NorthernHemisphere, namely North America andEurasia, for the period 197394 show a sta-tistically significant annual decline butmaximum (winter) snow cover extentshows no significant changes (Groismanet al. 1994). Brown (1997) has confirmed acentury-long decrease in spring snow coverin Eurasia, but the trend in North Americanspring snow cover for this period was notstatistically significant. Preliminary analysis

of a 24-year record (19792003) in snowextent derived from visible and passivemicrowave satellite data indicates a decreaseof approximately 35% per decade duringspring and summer (Frei & Robinson 1999;Armstrong & Brodzik 2001).

Snow depth data, which are more numer-ous, are notoriously unreliable unless associ-ated density values are reported. In much of Russia, for example, March is the monthwith maximum values of snow depth

FIG. 3.15 Mean annual snow accumulation (in centimeters) in the Columbia River basin as afunction of physiography (M. Church personal communication).

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(Kopanev 1982) but snowmelt floods occurin May over the Great Russian Plain and in June and July further to the northeast(Koren 1991). Snow water equivalentinformation has been analysed for the west-ern part of the FSU (Haggerty & Armstrong1996) and for the western USA (Cayan 1996).

Sturm et al. (1995) have developed a sea-sonal snow cover classification (Table 3.3)based on snow characteristics at time ofmaximum snow extent in each region.Snow classes identified were tundra, taiga,alpine, prairie, maritime, and ephemeral(Fig. 3.17), as well as a mountain categorywhich is highly variable and difficult to map at global and continental scales. Thesesnow classes provide an initial perception ofthe snow properties that can be expected in the regional ecosystems of the NorthernHemisphere.

Snow occurs in many different formsthat change rapidly in response to local

climatic conditions. Each climatic provincehas a prevailing snow type, which can becharacterized in terms of grain type andsize, hardness, density, layering, and depth(though it must be understood that there isalso great variety of snow types within eachregion). The kind of variation that is envis-aged is well illustrated by the three majorsnow regions of Alaska: tundra snow on theArctic slopes of the Brooks Range; taiga snowin the continental interior; and maritimesnow in the southern coastal mountains.

Seasonal snow accumulation varies amongclimate provinces and through the seasons.Years of excessive snowfall result in trans-portation delays, large snowmelt floods,power line failure, collapse of buildings, andproperty loss from snow avalanches. Yearsof little snowfall result in groundwaterdepletion, reduced surface water supply, lackof soil frost protection for agriculture, andlosses in the winter recreational industry.

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FIG. 3.16 Variation in ground snow-loads in British Columbia (Schaerer 1970).

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TABLE 3.3 Description of snow classes (according to Sturm et al. 1995).

Snow cover Description Depth Bulk Numberclass range density of layers

(cm) (g cm3)

Tundra A thin, cold, windblown snow. Maximumdepth, ~75 cm. Usually found above or northof treeline. Consists of a basal layer of depth 1075 0.38 06hoar overlain by multiple wind slabs. Surfacezastrugi common. Melt features rare.

Taiga A thin to moderately deep, low-density, cold 30120 0.26 15snow cover. Maximum depth, 120 cm. Foundin cold climates in forests where wind, initialsnow density, and average winter airtemperatures are all low. By late winter,consists of 5080% depth hoar covered bylow-density new snow.

Alpine An intermediate to cold, deep snow cover. 75250 No data 15Maximum depth, ~250 cm. Often alternatethick and thin layers, some wind affected.Basal depth hoar common as well asoccasional wind crusts. Most new snowfallsare low density. Melt features occur but aregenerally insignificant.

Maritime A warm, deep snow cover. Maximum depth 75500 0.35 15can be in excess of 500 cm. Melt features(ice layers, percolation columns) very common.Coarse-grained snow due to wettingubiquitous. Basal melting common.

Ephemeral A thin, extremely warm snow cover. Ranges 050 No data 13from 0 to 50 cm. Shortly after it is deposited,it begins melting, with basal meltingcommon. Melt features common. Often consist of a single snowfall, which melts away; then a new snow cover reforms at the next snowfall.

Prairie A thin (except in drifts), moderately cold 050 No data 5snow cover with substantial wind drifting.Maximum depth, ~100 cm. Wind slabs anddrifts common.

Mountain, A highly variable snow cover, depending on No data Variablespecial class solar radiation effects and local wind

patterns. Usually deeper than associated typeof snow cover from adjacent lowlands.

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Processes in snow tend to be more com-plex than those in most other solid earthmaterials because snow is thermodynami-cally active and its phase changes constantly.Both its continuum properties (density,temperature, stress distributions) and itsmicroscale properties (crystal morphologyand crystal bonding) change in response to small changes in the environment. Snowcrystal metamorphism commences as soon asa snow flake falls on the ground. The strength,

deviatoric properties, and compressibility ofthe snow are critical in relation to snowbehavior.

3.4.3 SNOW COVER STRUCTURE

Snow stratification results from successivesnowfalls over the winter and processes that transform the snow cover betweensnowfalls (Fig. 3.18). There are two inter-acting processes that transform the snow

FIG. 3.17 Snow class distribution over Eurasia and North America. Classes are described inTable 3.3 (from Groisman & Davies 2001).

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FIG. 3.18 (a) Use of symbol to describe a snowpack. (b) Classifications and symbols for snow measurements and surface conditions (NRC 1954).

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cover: snow settling and snow metamor-phism (Jones, H. G. et al. 2001). Becausemost of the physical properties of snowstrongly depend on snow density and onthe type of and size of grains comprising asnow layer, settling and metamorphism arefundamental. Snow metamorphism depends

on temperature, temperature gradient, andliquid water content. The distinction betweendry and wet snow is critical and the clas-sification of snow grains under the influ-ence of metamorphism is useful where aprocess-sensitive classification can be found(Table 3.4, Fig. 3.19).

TABLE 3.4 International classification of snow crystals (adapted from Colbeck et al. 1990).

Basic classification Shape Process classification

1. Precipitation particles a) Columns ag) Cloud-derived falling snowb) Needles h) Frozen rainc) Platesd) Stellar dendritese) Irregular crystalsf) Graupelg) Hailh) Ice pellets

2. Decomposing and a) Partly rounded particles a) Freshly deposited snowfragmented precipitation b) Packed shard fragments b) Wind-packed snowparticles

3. Rounded grains a) Small rounded particles ac) Dry equilibrium formsb) Large rounded particlesc) Mixed forms

4. Faceted crystals a) Small rounded particles a) Solid kinetic growth formb) Large rounded particles b) Early kinetic growth formc) Mixed forms c) Transitional form

5. Cup-shaped crystals and a) Cup crystal Hollow kinetic crystaldepth hoar b) Columns of depth hoar b) Columns of a)

c) Columnar crystals c) Final growth stage

6. Wet grains a) Clustered rounded grains a) No meltfreeze cyclesb) Rounded polycrystals b) Meltfreeze cyclesc) Slush c) Poorly bonded single crystals

7. Feathery crystals a) Surface hoar crystals a) Kinetic growth in airb) Cavity hoar b) Kinetic growth in cavities

8. Ice masses a) Ice layer a) Refrozen water above less-b) Ice column permeable layerc) Basal ice b) Frozen flow finger

c) Frozen ponded water

9. Surface deposits and crusts a) Rime a) Surface accretionb) Rain crust b) Freezing rain on snowc) Sun crust, firn-spiegel c) Refrozen sun-melted snowd) Wind crust d) Wind-packed snowe) Meltfreeze crust e) Crust of meltfreeze grains

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The size, type, and bonding of snow crys-tals are responsible for pore size and perme-ability of the snowpack. Depth hoar layershave large grains (210 mm) and pore sizesto match and are formed under high tem-perature gradient metamorphism. Roundedgrains are very small (0.10.4 mm) andpore size is small and there are no largevoids that would encourage collapse. In lowwind speed environments, fresh snowfallhas low hardness and a density range from50 to 120 kgm3. But density can changerapidly under metamorphism. Higher hard-ness is associated with high wind speeds,greater age, and concurrent blowing snowduring deposition.

Dry snow contains relatively little liquidwater, but the liquid-like layer that surroundsthe snow crystals is important to snowchemistry. Dry snow metamorphism is controlled by the vertical temperature gra-dient that develops because of radiativecooling at the surface, warm and/or wet soils,and the low thermal conductivity of thesnow cover. Temperature gradients inducewater vapor pressure gradients, vapor diffu-sion from the warmest crystals, and conse-quent change in the shape and size of thecrystals.

Wet snow is characterized by a significantamount of liquid water in snow and occursprimarily at mean snow temperatures at themelting point (Jones, H. G. et al. 2001).

The flow of water through snow isaffected by impermeable layers, zones ofpreferential flow called flow fingers, andlarge meltwater drains. Meltwater drains arelarge and extend to the base of the snowpack,whereas flow fingers usually initiate andterminate at snow layer boundaries. Theinhomogeneities of flow paths, water con-tent, and water flux during melt haveimportant implications for the chemistryand microbiology of snow covers.

Wet snow metamorphism involves therounding of snow crystals and theirenlargement at the cost of the smaller crystals. There is a consequent decrease incohesion and hardness as liquid bondsreplace solid ones between the crystals. Thedecrease of cohesion in wet snow inducesavalanche activity and changes the ability ofanimals to move over the snow surface andinside the pack. Denser and more cohesivesnow forms, and during active snowmelt,the density of a snowpack may change from 350 to 550 kgm3 in the course of a single day.


FIG. 3.19 Snowpack stratigraphy from a continental climate with the radiation recrystallized layerabove an ice layer and the weak faceted layer next to the ground (from McClung & Schaerer 1993).

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Basal ice forms where the meltwater flux exceeds the infiltration rate of frozensoils and there is a strong negative heat fluxfrom the snow to the soil. Such ice layersmay become quite thick (up to 70 cm) andpersist after the snow cover has melted, butthey are most prevalent in permafrostregions. Rain on snow events play a majorrole in wetting the snow surface and informing ice crusts. A change in the incidenceof rain on snow events could be an impor-tant indicator of climate change.

3.5 SNOW AVALANCHES

There are four distinct kinds of snow meta-morphism:

1 Destructive metamorphism of dry snow: A few days after deposition, the crystalshape is lost. Powder snow is fine grained(0.51 mm) with density between 0.15and 0.25 gcm3.

2 Constructive metamorphism of dry snow:Depth hoar has grain size between 2 and8 mm and density from 0.2 to 0.3 gcm3.

3 Melt metamorphism: This characterizeschanges produced in snow by the presenceof liquid water. Large polycrystallinegrains grow (c.15 mm) and rotten snowdevelops.

4 Pressure metamorphism: This is the densi-fication of dry neve on glaciers.

The character of snow avalanching in a general sense depends on the climate:whether maritime, continental, or transi-tional (McClung & Schaerer 1993; Table 3.5).A maritime snow climate has relativelyheavy snowfall, generates deep snow coverand experiences relatively mild tempera-tures. The maritime ranges of North America,for example, have average annual snowfallof 1015 m. Avalanche formation usuallytakes place during or immediately followingstorms and failures occur in the new snownear the surface (Fig. 3.19). Warm air pro-motes rapid stabilization of the snow nearthe surface. Rain immediately following deep,new snowfall is a major cause of avalanch-ing. Rainfall may also cause formation of icelayers, which may become future slidinglayers when buried by subsequent snowfall.

A continental snow climate has relativelylow snowfall, relatively shallow snow cover,and cold temperatures. A distinguishingfeature of avalanches in continental climatesis that they are often caused by buriedstructural weaknesses in older layers of thesnow. Recrystallization weakens old snowin the presence of cold temperatures andhigh-temperature gradients. The low tem-peratures also allow structural weaknessesto persist.

There are, broadly speaking, two types ofsnow avalanches: loose snow avalanchesand slab avalanches. Loose snow avalanches

TABLE 3.5 Characteristics of maritime, transitional, and continental snow climates* (fromMcClung & Schaerer 1993).

Snow Total Air temperature Snow depth New snow avalanche precipitation (C) (cm) densitytype (mm) (kg m3)

Maritime 1,280 1.3 190 120Transitional 850 4.7 170 90Continental 550 7.3 110 70

* Mean values compiled from 15 winters of US data (Armstrong & Armstrong 1987).

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start at or near the surface and only surfaceand near surface snow is involved. Slabavalanches are initiated at depth in thesnow cover and are usually more dangerous.Dry loose snow avalanches are triggered bya local loss of cohesion due to either meta-morphism or the effects of sun or rain. Wetloose avalanches are usually triggered byheavy melt due to sun or rainfall.

Dry slab avalanches are responsible formost of the damage and fatalities fromavalanching. The most common trigger fornatural dry slab avalanches is addition ofweight by new snowfall, blowing snow, orrain. Wet slab avalanches occur by threeprincipal mechanisms: loading by new precipitation; changes in strength of aburied weak layer due to water; and bywater lubrication of a sliding surface. Slushavalanches are a class of wet slab avalanchewhich form under the following conditions:rapid onset of snowmelt in spring; snow-pack is usually partially or totally saturated;and depth hoar is usually present at thebase of the snow cover. They occur com-monly in northern Scandinavia and Alaska.

Information on hazards posed by snowavalanches are important to developmentand winter sports (Fig. 3.20). Failure crite-ria for snow on steep slopes and the dynam-ics of avalanche motion are extremelycomplex phenomena which are largelymodeled by empirical approximations.

3.6 SNOWMELT, RUNOFF, AND STREAMFLOWGENERATION

Snow cover provides an insulating blanketover soil and lake ice for the winter periodand provides an important episodic flux oflatent heat, water, and chemicals into soilsand water bodies during the spring melt. Inmany high-latitude and high-altitude envi-ronments, snowfall accounts for over 40%of the precipitation and the release as

meltwater over a few days can be the singlemost important hydrological event in theseenvironments.

In the Coast Mountains of BritishColumbia, the contrast in the duration ofsnowmelt and glacier melt-dominated runoffis illustrated from the examples of MillerCreek (glacier melt) and Central Creek(snowmelt) (Fig. 3.21).

Meltwater infiltration into unfrozen soilsusually occurs in environments with deepsnowpack or maritime climates. Soils frozento depths of 15 cm behave as unfrozensoils with respect to infiltration. The pro-portion of snowmelt that infiltrates tounfrozen soils depends on the applicationrate of the snowmelt, the hydraulic conduc-tivity of the soil layers, and the water reten-tion characteristics of the soil. Dischargerates in excess of the saturated hydraulicconductivity will infiltrate until the soilbecomes saturated and ponding of water atthe base of the snowpack occurs.

Frozen soils develop where snow cover isthin or extremely cold winter temperaturesprevail. Frozen soils normally have lowerinfiltration capacities than unfrozen soils ofsimilar saturation level because the presenceof ice reduces the effective porosity of thesoil. However, frozen soils that contain largecracks or macropores accommodate infiltra-tion of all snowmelt water.

Although modeling the physical processof melt and the movement of water throughthe snowpack enables an accurate estimateof snowmelt at a site, several difficultiesarise in predicting snowmelt at a basin scale.

First, snowpack and meteorological con-ditions vary from place to place, especiallyin mountain environments. Second, mostbasins do not have a suitable network formeteorological snowpack data collection toallow physical modeling and third, longerterm estimates can only be based on theassumption that past averages can be repre-sentative of future states.

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The US Army has produced a set of basinsnowmelt equations which are based onenergy balance considerations, but empiricallyderived indices are substituted for analyticalsolutions. Thus they have produced empiricalequations for snowmelt from (a) open areas;(b) forested areas; and (c) for melt duringrain. Figure 3.22 is an example of one suchsetting (from Braun & Slaymaker 1981).

A cruder method (the degree day method)attempts to relate snowmelt exclusively toair temperature. The degree-day is definedas the number of degrees departure from areference temperature (commonly 0C) fora 24-hour period. Simplicity is both an assetand a shortcoming of this method.

A third method which is often used is amultiple regression method. Statisticallyderived equations relating snowmelt to allavailable independent variables, such aslongwave and shortwave radiation, windand temperature, wind and humidity, andrainfall and temperature, are combined toproduce the best predictive equation. Ingeneral, the choice of an approach dependson the availability of data, the type of pre-diction needed, the generality of the model,the scale of operation, and the resourcesavailable to the investigator.

Beyond the problem of extrapolatingsnowmelt from sites to regions is the issuethat snowmelt results from two quite

FIG. 3.20 Snow avalanche accident site with organized rescue (from McClung & Schaerer 1993).

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FIG. 3.22 Spatial and temporal distribution of snowpack as a function of elevation and vegetationcover in Miller Creek basin, Coast Mountains British Columbia (May to June 1978; from Braun &Slaymaker 1981).

FIG. 3.21 Runoff from snowmelt and glacier melt in Miller Creek basin, Coast Mountains, BritishColumbia, 1978.

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distinct sets of processes: the snowmelt processes themselves and the controls onrunoff production. Especially in mid-latitudes,the effects of the snowpack are minor. Byfar the most important control on the sizeand form of basin outflows is the interac-tions at the interface between the snow andthe soil. This is particularly true in forestswhere soils are porous, usually deep, andpermeable even when frozen. Price et al.(1978) presented an interesting contrastbetween snowmelt production at Knob Lake,Quebec, in the Canadian subarctic and atPerch Lake, Ontario. At Knob Lake, infiltra-tion rates were essentially zero because ofthe presence of frozen soils even after thesnowpack had completely melted. By con-trast, at Perch Lake the most important con-trols on snowmelt runoff are the hydrologicproperties of the soil. Even under low wintertemperatures honey comb frost conditionsprevail in the soil, thus allowing continuinginfiltration of meltwaters. These two examplesdefined two end points in the generation ofsnowmelt runoff: no infiltration and totalinfiltration.

Marsh and Woo (1981) extended this dis-cussion to High Arctic streamflow regimesby identifying that runoff in the High Arcticenvironment is sustained by various sourcesof water, including spring snowmelt, themelting of semipermanent snow banks,glaciers, and rainfall. If spring melt domi-nates, a simple Arctic nival regime resultsand if this is followed by summer glaciermelt, a proglacial flow regime results. Insome nonglacierized basins, however, ifsnowmelt is delayed until mid-summer or ifsemipermanent snow banks are abundant,a proglacial type of runoff pattern can beproduced. The overall result is that variouscombinations of several sources of waterwill generate a suite of runoff regimes thatrange from the simple nival to the typicalproglacial regime.

3.7 SNOW CHEMISTRY

Snowfall contains crustal elements such asCa and Mg, from terrigenous dust, anthro-pogenic pollutants, weak organic acids,neutral organics from natural sources, andtrace metals. These chemical species areincorporated in snow by three main pro-cesses: imprisonment during initial forma-tion of ice crystals; capture of gases, aerosols,and larger particulates in clouds; and scav-enging below the cloud layers during snow-fall (Fig. 3.23).

The chemical composition of snowfalldepends on factors such as the origin of theair masses that are scavenged, the altitudeat which snow is deposited, and the meteo-rological conditions during snowfall.Chemical concentrations often decreaseexponentially with time because of the pro-gressive scavenging that occurs during asnowfall event. A scavenging coefficient ,is used to characterize the efficiency of pre-cipitation in removing pollutants from theatmosphere.

Maritime air masses give snow with Naand Cl, whereas polluted air masses fromindustrial areas deposit acid snow with NO3and SO4 prominent. In general, snowfall athigher altitudes has lower concentrations ofchemical species because the depth of airavailable for scavenging is smaller.

The temporal and spatial variation in the chemical composition of snowfall pro-duces a snow cover that is chemically heterogeneous. Modification of that snowcover occurs through surface exchange at the snowatmosphere interface, surfaceand subsurface chemical reactions, snowgrain metamorphism within the pack, andbasal exchange processes at the snowsoilinterface.

Leaching of snow grains by meltwatercauses fractionation of solute species andthe meltwater front becomes progressively

84 CHAPTER 3

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more concentrated as it moves through thesnowpack. Deeper snow increases the dura-tion of snowmeltwater interaction andgives rise to higher snowmelt concentrations.Meltwater flowing through macropores orflow fingers is more dilute than melt flow-ing through the matrix, because of theshorter contact time.

There is an interesting interaction betweengrowth of algal biomass and the tendencyfor increasing concentration of nutrients inmeltwaters. Low amounts of free waterlower algal activity and nutrients may thenaccumulate.

3.8 SNOW ECOLOGY

Percolation of meltwaters through the snowcover causes the chemical composition ofboth the snow matrix and the meltwaters to change (Fig. 3.24). The concentration

and distribution of solutes in the snowmeltwater system is controlled by a varietyof physical and biological processes. Theseprocesses are the leaching of solute fromsnow grains, meltwaterparticulate interac-tions, and microbiological activity. There arealso snowatmosphere exchanges whichincrease the solubility of certain species,such as SiO2, HNO3, and HCl, in water.

The leaching of solutes from snow crys-tals, known as solute scavenging, results inincreasingly concentrated snowmelt as itmoves through the snowpack. The result of several diurnal meltfreeze cycles is oftento increase the concentration of ions in thefirst meltwaters issuing from the snowpack.

Chemical reactions between meltwaterand inorganicorganic particles can affectthe concentration of solute in meltwater.For example, snowmelt acidity may be neutralised by carbonaceous dust. In addi-tion, organic debris, such as leaf litter, may

FIG. 3.23 The main physical and chemical processes that influence the physical composition ofcold dry snow cover during accumulation (from Tranter & Jones 2001).

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encourage ionic exchange between melt-water and itself (Walker et al. 2001).

Microbiological and invertebrate activityin snow covers is stimulated during springmelt. Photosynthesis results in an increasein algal biomass at the expense of nutrientconcentrations in the meltwaters. The lossof nutrients in snowmelt waters over thewhole melt season may approach 30% insome years (Jones 1991). Microorganismssuch as bacteria, algae, and fungi are commonly found thriving in snow, glacialice, lake ice, ice shelves, and sea ice. They aresuitable habitats for microorganisms onlywhen liquid water is present for at least partof the year. The sea ice of the Arctic Oceanoften contains blooms of snow algae duringthe summer (Gradinger & Nurnberg 1996).Microbes are abundant in snow fields and indepressions and ponds found on glaciers(Wharton et al. 1985). Microorganisms playa fundamental role in the biogeochemistryof snow and ice and are closely involved in

the primary production, respiration, nutri-ent cycling, decomposition, metal accumu-lation, and food webs associated with thesehabitats (Hoham & Ling 2000).

The thermal properties of snow coveralso allow larger organisms to survive in therelatively benign microhabitats of the sub-nivean space. The snow cover, in this sense,is an ecotone between two different envi-ronments: the dry, very cold, windy, andchangeable atmospheric air and the humid,relatively warm, and stable air of the spaceunderneath.

3.9 GLACIER MELT

According to Benson (1961), there are fivezones on a glacier whose boundaries varyfrom year to year (Fig. 3.25):

1 A dry snow zone, where no melting occurseven in summer. The only dry snow zones

FIG. 3.24 The main physical, chemical, and biological processes that influence the chemical composition of snow cover during thaw (from Tranter & Jones 2001).

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occur in the interiors of Greenland andAntarctica and near the summits of thehighest mountains in Alaska, Yukon, andcentral Asia.

2 A percolation zone which starts below thedry snow line. Water can percolate a cer-tain distance into snow at temperaturesbelow 0C. Refreezing of meltwater is themost important factor in warming the snow.

3 A wet snow zone, which starts below thewet snow line.

4 A superimposed ice zone, which starts atthe snow line (firn line or annual snowline). At these lower elevations, so muchmeltwater is produced that the ice layersmerge into a continuous mass (superim-posed ice). This is the boundary betweenfirn and ice on the glacier surface at theend of the melt season.

5 All five zones may be found in parts ofGreenland and Antarctica. The majorAntarctic ice shelves have only dry snowand percolation zones and the entire massloss results from calving of icebergs and asmall amount of melting at the base. Bycontrast, the Barnes Ice Cap on Baffin

Island appears to have only superimposedice and ablation zones in most years. All ofthe above are cold glaciers in that thetemperature is below the melting point.

In a temperate glacier, the ice is at themelting point throughout, except for a sur-face layer, about 10 m thick, in which thetemperature is below zero for part of theyear. Temperate glaciers cannot have perco-lation zones because in that zone, by defini-tion, the temperature of deeper layers neverreaches 0C. Superimposed ice forms onlywhen the firn temperature is below 0C. On a temperate glacier, there can thereforebe only a limited superimposed ice zoneand the equilibrium and snow lines effec-tively coincide. A temperate glacier there-fore has only wet snow and ablation zones(Plate 3.2; Fig. 3.26). The Salmon Glacier inthe northern Coast Mountains of BritishColumbia and the South Cascade Glacier inthe North Cascades, Washington State dis-play the snow line and the wet snow andablation zones quite clearly.

FIG. 3.25 The five zones of a glacier: dry snow, percolation, wet snow, superimposed ice, ablationzone (from Muller 1962).

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3.10 FORMATION OF AN ICE COVER

Lock (1990) has a detailed discussion of the growth and decay of ice. The simplestcase is that of a shallow pond of quiescentwater on which a thin type of ice cover,which is usually transparent, will form. It isa continuous sheet of polycrystalline icecalled skim and can be seen on pools, reser-voirs, lakes, and sluggish streams (velocities0.5 ms1). Deeply supercooled aqueoussolutions generate downward growing den-drites, commonly known as candle ice.

More dramatic changes occur when thewater is not calm. Turbulent exchangeinterrupts the undisturbed crystal growthand multiplies the effective nucleation rate,thus creating a greater number of ice crys-tals. The crystals which grow under theseconditions are known as frazil ice. A sinter-ing mechanism generates first clusters andthen larger aggregates of frazil crystals.They are able to stick to any submerged

solid surface, and when they do so, theybecome anchor ice. This anchor ice, whichmay be as much as 1m thick, has the abilityto lift rocks and plants off the river bottomwhen it eventually is released. At the sametime, the unattached flocs of frazil generateslush balls or slush patches. The increas-ingly congealed slush then forms into pan-cake ice (Plate 3.3) and, in turn, pancakesmay collect and freeze together to produceice floes. In the ocean, the consolidation ofpancakes into floes eventually leads to acontinuous sheet of primary sea ice (cf.Table 1.3).

Further growth may take two forms: sec-ondary ice that is produced on the bottomof the ice cover either by direct freezing orby the accretion of frazil or superimposedice that forms on top of the ice cover whenit is inundated with water. The consolida-tion and jamming of primary river ice maycause sudden flow surges which can lead toinundation and this kind of superimposed

FIG. 3.26 Change in length of the South Cascade Glacier between 1960 and 1983. The ablationzone is clearly distinguished from the wet snow zone (photograph by Andrew G. Fountain).

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ice is called naled. The other major sourceof superimposed ice is snow. If snow falls on open water, the snow crystals providenucleation sites for the production of fraziland if the snowfall is heavy enough, it maylead to a slush cover from which primaryice may be formed. Later in the season,snow falling on discrete pieces of surface ice(skim, plates, pancakes, or floes) introducesadditional mass. The additional weight ofthe snow causes the ice to submerge andwater is added to the ice by capillarity.Subsequent freezing creates snow ice orwhite ice.

Bottom ice growth by addition is stronglyinfluenced by the temperature and velocityfields below. Once a continuous sheet of icecovers a large area of sea, lake, or river, theprocess of secondary growth will depend on the removal of latent heat upwardsthrough the cover; this is thermal growth or congelation.

Open fault lines commonly occur in thefirst-year ice and can be detected as brightfeatures in radar images (Fig. 3.27).

3.11 RIVER AND LAKE ICE

Prowse (2000) has provided a useful summaryof river ice ecology in four parts: (i) autumncooling; (ii) freeze-up; (iii) main winter;and (iv) break-up (Figs. 3.28 and 3.29).Under autumn cooling, the formation of ice can reduce the amount and quality ofwinter habitat or can create new refugia.For example, border ice develops along themargins and offers a low velocity refugeand protection from predation. Frazil icehowever can be repellent as it abrades the gills and may cause suffocation. Anchorice may cause death of benthic inverte-brates and some fish and may also promoteice growth into the spawning areas orredds. Accumulation of anchor ice can alsoalter riverine habitat by modifying pool

depth. Anchor ice commonly attaches toaquatic vegetation, boulders, and areas ofgravel and coarse sand and when it isreleased, these materials and benthic organ-isms are carried downstream.

Under freeze-up, complete freeze-up ofthe water surface occurs. Loss of river dis-charge to channel storage occurs behind the advancing freeze-up front eventuallyleading to backwater flooding of riparianzones. During the main winter, isolatedpool and ice-cavity habitats develop. Theseare critical for the survival of fish andaquatic plants.

FIG. 3.27 Prince Patrick Island NorthwestTerritories, Canada. The photograph was takenon July 2, 1982, during the FIREX/RADARSATfield experiment. The faults formed in Junewhen solar heating had raised the temperatureto within a few degrees of 0C. Puddling on the ice from the melting of snow and ice wasnear maximum. Remnants of barchan snowdunes can be seen; most of the higher groundin the photograph is due to deeper snow cover(photograph by Arnold M. Hanson).

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FIG. 3.29 Dynamic transition in processes and habitat created by rapidly moving break-up front ofriver ice (from Prowse 2000).

FIG. 3.28 Example of winter changes in dissolved oxygen. (1) Slight increase as water cools. (2) Peak prior to freeze over. (3) Rapid decline after complete freeze over. (4) and (6) Steady declineas groundwater contribution increases. (5) Mid-winter increase from runoff event. (7) Seasonalminimum. (8) Rise in oxygen production with increased photosynthesis. (9) Brief supersaturationwith turbulence with break-up. (10) Return to open water levels (from Prowse 2000).

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When the ice cover is fully developed(Plate 3.4), DO levels are significantlyreduced. By the time that very low levels ofDO are achieved, fish will have abandonedmost reaches of the rivers and concentratedin a few preferred zones, where theyremain relatively inactive for the rest of thewinter. They are also vulnerable because ofovercrowding and lack of oxygen for indi-vidual fish.

The presence of an ice cover has impor-tant implications for the transporting of dissolved and suspended substances. Icealso, in the form of frazil ice, is an agent of sediment transport. Its greatest transportpotential is as large anchor ice deposits thatdetach from the bed, transporting bouldersas large as 30 kg. As a result of decreasedsediment transport capacity, there is a tendency for deposition of large amounts offiner sediment.

The break-up period is the most dramaticfrom the perspective of environmentalchange. Breaking fronts on large rivers maytravel at 5 ms1 and water levels may riseat 1 mmin1. The issue of the role of break-up discharge in the long-term formation ofnorthern rivers is still a topic of contention.For many northern rivers, ice break-up occursconcurrently with the spring freshet and thisis often the major hydrologic event of theyear for sediment transport (Church 1974).

3.12 SEDIMENT BUDGETS

The sediment mass balance equation isdefined as

Ss = Is Os (3.17)

where Ss is sediment stored in a drainagebasin on slopes, floodplains, in channels, inlakes, and in ice; Is is sediment produced byweathering or from dynamic sedimentsources; and Os is sediment exported from

the system (Reid & Dunne 1996; Slaymaker2004). This sediment balance must ulti-mately be zero. But in most real worldexamples it is not zero, certainly not in largedrainage basins over short periods of time.The dimensions of the storage term, andwhether it is positive or negative, is ofimportance in the assessment of sedimenttransport at all spatial and temporal scales.

Interpretation of the storage term fromlacustrine or marine sedimentation recordsis at first sight rather straightforward. Forexample, in the case of the Black Sea sedi-ments, large sediment stores have beenidentified with the period of deglaciationsince the LGM (157 ka BP) and the last 2 ka, when the tributary basins have beenextensively disturbed by human activities(Shimkus & Trimonis 1974). But when theprocesses of redistribution of sedimentwithin river basins are considered, itbecomes evident that storage is more com-plex. Sediment on its way from source tosink gets side-tracked in a number of ways.The issue is similar in principle to thatwhich was discussed earlier with respect to snowmelt runoff pathways, but morecomplex because sediment transfer is anintermittent process. It is in this contextthat the concept of virtual velocity of sed-iment transfer is useful. Virtual velocity canbe defined as the velocity of sediment trans-fer through a reservoir and is simply theinverse of the residence time per meter ofreservoir length. Residence time per meterequals the mapped volume of sediment permeter divided by the bedload discharge rate.

The most dramatic primary sediment dis-turbances to visit Earths surface within thepast 2 Ma have been the recurrent, unstablePleistocene glaciations of the NorthernHemisphere. Church and Slaymaker (1989)have depicted a relatively rapid virtualvelocity for sediments in the deglaciatedupland streams of British Columbia whereassediments moving through the major river

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valleys have extremely slow virtual veloci-ties. So slow indeed that they envisage dis-equilibrium of the fluvial sediment massbalance over the time scale of the whole ofthe Holocene epoch (of order 10 ka).

Contemporary glacial sediment yield isrelatively high (Plate 3.5). Glaciers areeffective abraders of subjacent rock and soilmaterials may be frozen into the glacier andon to the glacier sole. At the ice margin,material that melts out from the ice, or isexposed from below the ice, is immediatelysusceptible to transport by wind and water(Church 2002). The freezethaw environ-ment and absence of vegetation cover mag-nify the effectiveness of these processes.Consequently, it has been established (Hallet

et al. 1996) that maritime glaciation onNeogene rocks in southeastern Alaska mustyield some of the highest glacial sedimentyield rates on Earth (Table 3.6). As onemoves away from the glacier margin, spe-cific yields decline. Persistently decliningyields indicate either a concentration ofsediment sources at the headwaters of thesystem or aggradation downstream (Church2002, p. 103). Over a very long time, thedistinctive spatial signature of fluvial sedi-ment transfer following a major glacial dis-turbance diffuses downstream. The signatureof disturbance in time is similar and hasbeen designated paraglacial sedimentyield (Church & Ryder 1972; Slaymaker1987; Church & Slaymaker 1989).

TABLE 3.6 Fluvial sediment response to disturbance (after Church 2002).

Site/event Ad* t tr Intensity Magnification||

(km2) (a) (a) (tonnes km2 a1)

Contemporary glacial 48 400 27Nigardsbreen, Norway 38 1,050 66Engabreen, Norway 1,000 70Iceland (summary) 50100Alaska (summary)** 1,000 100

Pleistocene glacialBritish Columbia 104 104 ~102 102

* Area of drainage basin above measurement point for sediment transport, or at area of peak response (B.C.). Response time. Relaxation time. Sediment yield at peak response.|| Intensity/weathering rate (the denominator generally being estimated). Mainly after Hallet et al. (1996).** From Guymon (1974).

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4

PATTERNS OF THE CONTEMPORARYCRYOSPHERE AT LOCAL TO

GLOBAL SCALES

4.1 INTRODUCTION

In this chapter, we illustrate the dramaticways in which remote sensing and satelliteimagery have revealed spatial patterns of thecryosphere. Remote sensing of cryosphericsystems is a rapidly evolving field (Duguay& Pietroniro 2005). Remote sensing is animportant tool for many different scientificand practical applications related to snow,permafrost, and ice cover. Unfortunately,not all the variables can be retrieved withsufficient accuracy at the spatial and tempo-ral scales provided by current spacebornesystems. There is a need for higher spatialresolution without decreasing temporal resolution (Duguay & Pietroniro 2005).Topography and different land cover typesaffect retrieval of snow, permafrost, and icevariables by remote sensing partly becausetopographic and atmospheric processes arenonlinearly coupled and partly because ofour limited understanding of the complexradiation interactions between snow, bareground, and vegetation.

4.2 REMOTE SENSING OBSERVATIONS

In Chapter 2, we described the data sets thatrepresent, in general, point-scale cryosphericprocesses. Geographical Information Systems,

as formal software (commercially available)and informal software (using linked open-source programing language routines), areeffective at managing these in situ datasources. They can also be used to estimate,or interpolate, what processes might befound between in situ measurements.However, by their nature, the measurementsrepresent cryospheric conditions at a pointand unless appropriate spatial samplingdesigns are used, interpolation carries uncer-tainty with it. Remote sensing instrumentshave the capability to measure indirectlycryospheric variables over continuous geo-graphical space. Remote sensing instrumentsdo not generally measure directly cryosphericvariables (snow depth, ice velocity, etc.).Rather they measure the electromagneticenergy emitted or reflected (or backscattered)from the variable of interest which then hasto be converted to a geophysical quantity ofinterest. There are many excellent textsavailable on the fundamentals of theory ofremote sensing and the reader should lookto these for a full description of currentmethods (Schowengerdt 1997; Henderson& Lewis 1998; Campbell 2002; Lillesand et al.2003; Ustin 2004). Some excellent remotesensing resources are also available on theinternet (e.g. http://www.earthobserva-tory.nasa.gov/ http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.php).

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Among the principal national and inter-national organizations that conceive,develop, launch, and maintain civiliansatellite remote sensing instruments are theNational Aeronautics and Space Admini-stration (NASA), the European Space Agency(ESA), the Japan Aerospace ExplorationAgency (JAXA), and the Canadian SpaceAgency (CSA). Many other countries suchas Argentina, Brazil, India, and China alsohave capabilities in developing and buildingsatellite instruments. To coordinate interna-tional civil spaceborne missions designed to observe and study planet Earth, theCommittee on Earth Observation Satellites(CEOS) was created (http://www.ceos.org). It is composed of 23 members (mostof which are space agencies) and 21 associ-ates (associated national and internationalorganizations). A recommendation in 1984from the Economic Summit of IndustrializedNations Working Group on Growth,Technology, and Employments Panel ofExperts on Satellite Remote Sensing wasthe initial impetus for the formulation ofCEOS. The group recognized the multidisci-plinary nature of satellite Earth observationand the value of coordination across all pro-posed missions. CEOS has established abroad framework for coordinating all space-borne Earth observation missions. Individualparticipating agencies make their best effortsto implement CEOS recommendations. Themain goal of CEOS is to ensure that criticalscientific questions relating to Earth obser-vation and global change are covered andthat satellite missions do not unnecessarilyoverlap each other.

The fundamental unit of electromagneticradiation is the photon. Photons move atthe speed of light as waves, analogous to theway waves propagate through the oceans.The energy of a photon determines the fre-quency (and wavelength) of light that isassociated with it; the greater the photonicenergy, the greater the frequency of light

and vice versa. The entire array of electro-magnetic waves comprises the electromag-netic (EM) spectrum. The EM spectrum isdivided into continuous regions to whichdescriptive names have been applied.Gamma rays and x-rays exist at the veryenergetic levels (high frequency; short wave-length) while radio waves exist at the lowenergy levels of the spectrum. The visibleregion occupies the range between 0.4 and0.7 m (micrometer), or its equivalents of400700 nm (nanometer) and the infrared(IR) region spans between 0.7 and 100 m.The microwave region is located between1 mm and 1 m. These three mid-rangewavelength bands (visible, infrared, andmicrowave) are the areas of the EM spec-trum commonly exploited for ground, air-craft, and satellite Earth Observationinstruments. Gamma rays and x-rays do notpass through Earths atmosphere and socannot be used for Earth remote sensingmeasurements from satellite (althoughgamma rays are used on aircraft platformsfor certain applications see below). Also,low frequency radio waves do not, gener-ally, have enough energy to give a strongsignal when detected by satellites and sotheir use is also restricted to ground or air-craft studies. Visible and infrared (VIS/IR)measurements and microwave measure-ments have been the most exploited part ofthe EM spectrum for Earth observation fromground, air, and space. At these wavelengths,EM waves can pass through the atmosphereand be measured to give information aboutthe ground (or atmospheric) surfaces below.

There are two modes of detecting EMfrom ground aircraft or spacecraft platforms.First, passive instruments detect naturalenergy that is reflected or emitted from theobserved scene. They sense only radiationemitted by the object being viewed orreflected by the object from a source otherthan the instrument. Reflected sunlight is the most common external source of

94 CHAPTER 4

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radiation sensed by passive instruments.Scientists use a variety of passive remotesensors. For example, a radiometer is aninstrument that measures the intensity ofelectromagnetic radiation in some band ofwavelengths in the spectrum. Usually aradiometer is further identified by the por-tion of the spectrum it covers; for example,visible, infrared, or microwave. An imagingradiometer is similar to a static radiometerexcept that it includes a scanning capabilityto provide a two-dimensional array of mea-surements from which an image may be pro-duced. A spectrometer detects, measure, andanalyzes the spectral content of the incidentelectromagnetic radiation while a spectrora-diometer measures the intensity of radiationin discrete multiple and sometimes very finewavelength bands (i.e. multispectral).

The second mode of remote sensinginstrument is the active instrument thatprovides its own energy (electromagneticradiation) to illuminate the target or scenethat is then observed. Active instrumentssend a pulse of energy from the sensor tothe object and then receive the radiationthat is reflected or backscattered from that object. For example, a Radar (RadioDetection and Ranging) transmits radiowaves or microwaves through a directionalantenna and a receiver measures the timeof arrival of the reflected or backscatteredpulses of radiation from distant objects.Scatterometers are high frequency activemicrowave radars designed specifically tomeasure backscattered radiation. Whilescatterometers are generally nonimaging incharacter, they can be used to build maps of a required geophysical variable. Radaraltimeters send out single microwave pulsesof energy and measure the reflected radia-tion from the surface. Lidars (LightDetection and Ranging) use a laser to trans-mit visible light pulses the reflection fromwhich are detected by a receiver. Distanceto the object is determined by recording the

time between the transmitted and backscat-tered pulses and using the speed of light tocalculate the distance traveled. Finally, laseraltimeters use lidars to measure the heightof the instrument platform above the sur-face. By independently knowing the heightof the platform with respect to the meanEarths surface, the topography of theunderlying surface can be determined.

The following sections describe remotesensing measurements that are us

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